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Full-Text Articles in Engineering
A Computer-Aided Diagnosis System For Breast Cancer Detection By Using A Curvelet Transform, Nebi̇ Gedi̇k, Ayten Atasoy
A Computer-Aided Diagnosis System For Breast Cancer Detection By Using A Curvelet Transform, Nebi̇ Gedi̇k, Ayten Atasoy
Turkish Journal of Electrical Engineering and Computer Sciences
The most common type of cancer among women worldwide is breast cancer. Early detection of breast cancer is very important to reduce the fatality rate. For the hundreds of mammographic images scanned by a radiologist, only a few are cancerous. While detecting abnormalities, some of them may be missed, as the detection of suspicious and abnormal images is a recurrent mission that causes fatigue and eyestrain. In this paper, a computer-aided diagnosis system using the curvelet transform (CT) algorithm is proposed for interpreting mammograms to improve the decision making. The purpose of this study is to develop a method for …
Extracting Fuzzy Rules For The Diagnosis Of Breast Cancer, Ali̇ Keleş, Aytürk Keleş
Extracting Fuzzy Rules For The Diagnosis Of Breast Cancer, Ali̇ Keleş, Aytürk Keleş
Turkish Journal of Electrical Engineering and Computer Sciences
About one million women are diagnosed with breast cancer every year. Breast cancer makes up one-third of all cancer diagnoses in women. Diagnosing breast cancer early is vital for successful treatment. Among the breast cancer screening methods available today, mammography is the most effective, although the low precision rate of breast biopsy caused by mammogram interpretation results in approximately 70% unnecessary biopsies with benign outcomes. The aim of this study was to extract strong diagnostic fuzzy rules for the inference engine of an expert system to be used for the diagnosis of breast cancer. These rules have been extracted through …
Detection Of Microcalcification Clusters In Digitized X-Ray Mammograms Using Unsharp Masking And Image Statistics, Peli̇n Kuş, İrfan Karagöz
Detection Of Microcalcification Clusters In Digitized X-Ray Mammograms Using Unsharp Masking And Image Statistics, Peli̇n Kuş, İrfan Karagöz
Turkish Journal of Electrical Engineering and Computer Sciences
A fully automated method for detecting microcalcification (MC) clusters in regions of interest (ROIs) extracted from digitized X-ray mammograms is proposed. In the first stage, an unsharp masking is used to perform the contrast enhancement of the MCs. In the second stage, the ROIs are decomposed into a 2-level contourlet representation and the reconstruction is obtained by eliminating the low-frequency subband in the second level. In the third stage, statistical textural features are extracted from the ROIs and they are classified using support vector machines. To test the performance of the method, 57 ROIs selected from the Mammographic Image Analysis …